2020 blocks are larger in the Oakland area, so 2020 was used for the shapefiles.

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## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x
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## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x
## Warning: Expected 4 pieces. Additional pieces discarded in 1053075 rows [11, 12,
## 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, ...].
## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 55425 rows [1, 2,
## 9, 72, 73, 80, 143, 144, 151, 214, 215, 222, 285, 286, 293, 356, 357, 364, 427,
## 428, ...].
## Warning: Expected 3 pieces. Additional pieces discarded in 1365492 rows [11, 12,
## 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, ...].
## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 71868 rows [1, 2,
## 9, 73, 74, 81, 145, 146, 153, 217, 218, 225, 289, 290, 297, 361, 362, 369, 433,
## 434, ...].
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries

For the calculation of population change, I mapped the census blocks from 2010 to match the blocks of 2020, assuming proportional distribution of population within the census block areas.

I can’t quite figure out how to get the cookie cutter method to work.

density_change_oakland <- combined_oakland_blocks %>% select(block = GEOID20, perc_area, area_2020) %>% left_join( alameda_pop_race_2010, ) %>% mutate( pop_2010 = pop * perc_area, original_pop_2010 = pop, pop = NULL, race_2010 = race, race = NULL ) %>% left_join( alameda_pop_race_2020, by = c(“GEOID20” = “block”) ) %>% mutate( density_change = pop - pop_2010 )

Instead, I’ll just use matching GEOIDs for now and create the map.

## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'

I need to keep working on displaying the map properly, with a proper range and having layers for the races.

Reflect on your findings. Explain any key assumptions you made in the analysis, or caveats about the data sources that you think the reader should be aware of.